BUSCADOR RECOLECTA

Supplementary Figure 1: Control of immunolabelling and ACE2 expression in adult hippocampus. A-C) Pictures of the hippocampus fimbrial angle in control processed sections without primary ant-ACE2 antibodies: A) rabbit polyclonal anti-ACE2 (Abcam Cat# ab15348). Control sections processed with rabbit polyclonal anti-ACE2 (Sigma-Aldrich Cat#HPA000288) and mouse monoclonal anti-ACE2 (R&D Systems Cat# MAB933) were similar to this one (data not shown). B) rat monoclonal anti-GFAP (Millipore Cat# 345860-100UG). Control sections processed with goat polyclonal anti-Doublecortin (Santa Cruz Biotechnology Cat# sc-8066) were similar to this one (data not shown); C) rabbit polyclonal anti-TBR2 (Abcam Cat# ab23345). D-G) ACE2 expression has been detected in paraffine section of adult hippocampus, demonstrating specific expression of ACE2 (rabbit ati-ACE2 from Abcam) in astroglia in D, E and F (arrows), and pericytes in G (arrow). While ACE2-expressing cells are identified by brown-colored neurons were negative as we can observe in D by the white color (arrowhead). H) Double immunohistochemistry showing GFAP expression (brown) and ACE2 expression (black dots; arrows) in astroglial cells. I) Control section processed with GFAP and without ACE2 antibodies, showing the GFAP derived immunoreactivity and absence of ACE2 immunostaining. Scale bar: D-I) 50 µm. Supplementary Figure 2 is not the revised version and has to be changed by the uploaded one, which contains the revised lettering: Specificity of ACE2 expression in human lung, kidney and developing brain. A-C) The three antibodies show ACE2-specific immunoreaction in human lung in alveolar endothelial type II cells (arrows) and capillary endothelium (arrowheads). D-F) The three antibodies show ACE2-specific immunoreaction in human renal proximal tubule cells (arrows). G) Low power picture of a brain section processed in parallel by immunohistochemistry without primary ACE2 Sigma-Aldrich antibody and counterstained with Cresyl violet. H) High power picture of a brain section processed in parallel by immunohistochemistry without primary ACE2 R&D antibody and counterstained with Cresyl violet. Control sections without primary antibodies do not showed immunostaining. I-N) The three antibodies show ACE2-specific immunoreaction in DG progenitors and migrating cells in DGMS. Scale bar: A, B, C) 50 µm; D, E, F) 25 µm; G, I, K, M) 100 µm; J, L, N) 25 µm. DGMS: Dentate Gyrus Migratory Stream; IZ: Intermediate Zone; SVZ: Subventricular zone: VE: Ventricular Epithelium., Peer reviewed

Maximum monthly aggregate value of significant wave height for the Caribbean Sea from 1958 to 2017. The mean wave period, mean wave direction and the wind velocity (U10 and V10) related to the date of the maximum aggregation significant wave height value, are included in this dataset., Peer reviewed

-General information for the experimental section, NMR spectra, structural analysis, computational details and energies of optimized structures, experimental and computed UV/vis spectra, molecular orbitals, cyclic voltammograms, and normalized excitation and emission spectra (PDF) -Cartesian coordinates of the optimized structures (XYZ), Peer reviewed

Datasets used in the generation of figures 1 and 2 of: Melero, I., González, R., Elena, S.F. 2022. Host developmental stages shape the evolution of a plant RNA Virus. Philos. Trans. R. Soc. B doi: 10.1098/rtsb.2022.0005, Peer reviewed

Figure S1 Nucleotide sequences of PVX-wt (GenBank accession number MT799816.1) and the derived recombinant viruses PVX-VHH1, PVX-VHH1-P2A, PVX-VHH1-E2A and PVX-VHH1-F2A. Figure S2 Nucleotide sequences of TEV-wt (GenBank accession number DQ986288, including silent mutations G273A and A1119G in red) and the derived recombinant viruses TEV-VHH1, TEV-VHH1-P2A, TEV-VHH1-E2A and TEV-VHH1-F2A. Figure S3 VHH2 was cloned replacing VHH1 in all constructs., Peer reviewed

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until October 2021 whenever possible. We have archived the dataset into two parts. This first part contains the 4527 records that are can be used for both research and consultancy purposes. The higher-frequency sea-level dataset in this first part of GESLA-3 was obtained from 33 international and national data providers, specifically: University of Hawaii Sea level Center, National Oceanic and Atmospheric Administration, Marine Environmental Data Section, United States Geological Survey, Bureau of Meteorology, Rijkswaterstaat, Japan Oceanographic Data Center, Japan Meteorological Agency, Swedish Meteorological and Hydrological Institute, Réseaux de référence des observations marégraphiques (Reference networks for tidal observations), British Oceanographic Data Centre, California Department of Water Resources, Japan Oceanographic Data Center, Japan Coast Guard, Norwegian Hydrographic Service, Japan Oceanographic Data Center, Geospatial Information Authority of Japan, Wasserstraßen-und Schifffahrtsverwaltung des Bundes (Federal Waterway and Shipping Administration), Japan Oceanographic Data Center, Ports and Harbours Bureau, South Florida Water Management District, Instituto Superiore per la Protezione e la Ricerca Ambientale (Higher Institute for Environmental Protection and Research), Instituto Español de Oceanografía (Spanish Istitute of Oceanography), Data archaeology exercise, National Autonomous University of Mexico, Finnish Meteorological Institute, Danish Meteorological Institute, Bundesanstalt Für Gewässerkunde(Federal Institute of Hydrology), Marine Institute (Coastal sites), Coastal Channel Observatory, National Oceanography Centre, North West Florida Water Management Department, European Sea-Level Service, Icelandic Coast Guard Hydrographic and Maritime Safety Department, North Carolina Department of Emergency Management, Marine Institute (River Sites) and the Global Sea Level Observing System. These data are made available under the creative commons CC-BY 4.0 license., Peer reviewed

This dataset is a major update to the quasi-global, high-frequency (at least hourly) sea level dataset known as GESLA (Global Extreme Sea Level Analysis). Versions 1 (released 2009) and 2 (released 2016) of the dataset has been used in many published studies, across a wide range of oceanographic and coastal engineering-related investigations concerned with evaluating tides, storm surges, extreme sea levels and other oceanographic processes. The third version of the dataset (released 2021), presented here, contains twice the number of station-years of data (91021), and nearly four times the number of station records (5119), compared to version 2. The dataset consists of records obtained from 36 sources around the world, including some data archaeology efforts. The oldest record dates from year 1805 (spanning 217 years), followed by a few other stations starting during the 1840s and 1850s. However, the vast majority of records start during the 1950s. Data have been updated until mid-2021 whenever possible. We have archived the dataset into two parts. This second part contains the 592 records that are can be used for research purposes, but not consultancy. The higher-frequency sea-level dataset in this second part of GESLA-3 was obtained from 3 international and national data providers, specifically: City of Venice, Tide Forecasts and Reporting Center, University of Zagreb and the Copernicus Marine Environment Monitoring Service. These data are made available under the creative commons BY-NC 4.0 license., Peer reviewed

Figure S1: (a) Valve connected to the biodigester with the respective rings and screw nut; (b) Bottle lid showing the hose pipe connected after glue (T-Rex Flex the universal adhesive sealant, Soudal). Figure S2: Filter system assembly schematic. Figure S3: Air chamber and the valve core. Figure S4: Flame system with the air inlet open (a) and closed (b). Figure S5: Color changing of NaOH solution during the absorption process. Table S1: Material list and their respective images involved in the construction of the DIY biodigester. Video S1: Building a mini biodigester for schools., Peer reviewed

Table S1. Characteristics of the 11 studied ST274-Pseudomonas aeruginosa recovered from different origins; Table S2. Characteristics of the genomes of 14 reference ST274-P. aeruginosa strains and control strains PAO1 and PA14 downloaded from the NCBI database; Table S3. Single-nucleotide polymorphisms (number of SNPs) in the core genome among the 11 studied ST274-P. aeruginosa, the 14 reference ST274-P. aeruginosa genomes, and the two control genomes; Table S4. Pangenome determined using Roary in all 25 ST274-P. aeruginosa strains and the control strains PAO1 and PA14; Table S5. Acquired genes in ST274-P. aeruginosa with Resfinder prediction; Table S6. Dataset and function of the 170 antimicrobial resistance genes analyzed to determine the mutational resistome of the ST274-P. aeruginosa strains; Table S7. Mutational resistome of the 25 studied ST274-P. aeruginosa strains with respect to PAO1 genome; Table S8. Detection of alterations in the mutome genes (involved in the mutator phenotype) of the 11 studied ST274-P. aeruginosa strains and the 14 reference ST274-P. aeruginosa genomes, with respect to PAO1 genome; Table S9. Dataset and functions of the 250 virulence genes analyzed to determine the virulome of the ST274-P. aeruginosa strains; Table S10. Amino acid changes in virulome of the 25 studied ST274-P. aeruginosa strains with respect to the PAO1 genome., Peer reviewed

Additional file 1: Table S1. Requested and simulated transcript models and read counts for ONT and PacBio datasets used in SQANTI-SIM validation. Table S2 Simulated cDNA ONT and PacBio datasets used for pipeline benchmarking. Additional file 2: Figure S1. Number of detected true (TP) and false positives (FP) for different types of novelty (ISM, NIC, and NNC). Figure S2. Relationship between true positives (TP), false negatives (FN), and false positives (FP) with (a) transcript length, (b) number of exons, and (c) simulated expression level. Figure S3. SQANTI-SIM characterization of CAGE peak data. Additional file 3: Supplementary methods and code. Additional file 4: Peer review history., Peer reviewed